Currently, there exist numerous systems for providing recommendations to an end user such as Pandora.com, Last.fm, iLike.com or Amazon.com. Amongst these systems and others, there are some for recommending media files, such as, for example music and video.
Several systems currently available utilize exclusively the usage patterns of the user as an input to the system and then try to infer and make predictions of the preferences or taste of the user in order to make a recommendation. In the case of music media, for example, the usage pattern of the user may be the actual listening of songs.
In most current systems, once the preferences or tastes of a user have been determined through his usage pattern or usage history, an external database containing media is queried with these preferences in order to provide a list, often called a playlist, to the user. Many systems of the prior art propose applying digital signal processing (DSP) techniques for inspecting the medias in order to extract media relating properties which can then be used for querying a database of available medias and matching a subset of medias to constitute a recommendation for the user.
Therefore, many systems of the prior art analyze the usage history of the user, and attempt to discover or characterize the tastes of a user with DSP techniques. However, these systems have shortcomings as it is difficult to characterize media tastes of human beings and how these tastes relate to the properties of media files.
Furthermore, many systems of the prior art use a pre-analyzed and central database of media having a limited scope and size which limit the recommendations that can be provided to a user, since no single database can be maintained to contain all the medias ever produced.